Computational Methods for Detecting Multi-gene Relationships in Gene Expression Data

在过去的几年里，基因共表达已经成为一种强大的生物信息学工具。共表达的基因可能参与相同的生物过程，因此共表达网络经常被用来研究基因的功能。检测、分析和聚类共表达基因对的方法现在已经发展得很好了。然而，成对共表达显然过于简单化，无法描述基因之间的复杂关系，因为这些关系可能涉及多个基因，并可能因生物学背景而异。为了解决这一局限性，我们建议开发计算方法来检测和分析基因表达数据中的多基因关系，并使用这些方法来解决分子生物学中的问题。这些问题将包括基因调控网络的重建，基因调控调节器的检测，以及改进的基因功能预测工具。 检测多基因关系的主要困难之一是需要考虑大量可能的关系。这在拥有大量基因的生物体中尤其如此，例如植物和哺乳动物。在这种情况下，将真正的关系从可能的关系中分离出来会带来具有挑战性的计算和统计问题。为了解决这些问题，我们将首先开发低计算复杂度的方法来检测相对简单的多基因关系。为了避免数据过度拟合，我们将开发准确估计假阳性率的方法。我们还将开发启发式算法来加快计算速度，同时尽可能少地遗漏重要关系。为了验证这些方法，我们将在真实和模拟的基因表达数据上对它们进行测试。在这些结果的基础上，我们将开发方法来检测涉及更多基因的更复杂的关系。

Gene coexpression has emerged over the last several years as a powerful bioinformatics tool. Coexpressed genes may be involved in the same biological process, and thus coexpression networks are often used to investigate gene function. Methods for detecting, analyzing and clustering pairs of coexpressed genes are now well developed. Pairwise coexpression, however, is clearly too simplistic to describe the complex relationships between genes, since these relationships may involve multiple genes and can vary depending on the biological context. To address this limitation, we propose to develop computational methods for detecting and analyzing multi-gene relationships in gene expression data, and to use these methods to solve problems in molecular biology. These problems will include the reconstruction of genetic regulatory networks, the detection of modulators of gene regulation, and improved tools for gene function prediction. One of the main difficulties in detecting multi-gene relationships is the vast number of possible relationships which need to be considered. This is especially true in organisms with a large number of genes, such as plants and mammals. In such cases, separating the true relationships from the possible relationships poses challenging computational and statistical problems. To address these problems, we shall first develop methods of low computational complexity for detecting relatively simple multi-gene relationships. To avoid overfitting the data, we will develop methods for accurately estimating false positive rates. We will also develop heuristics for speeding up the computations while missing as few significant relationships as possible. To validate the methods, we will test them on both real and simulated gene expression data. Building upon these results, we will develop methods for detecting more-complex relationships involving larger numbers of genes.